Multi-Dimensional Systemic Risk Taxonomies

Systemic sustainability risk does not conform to traditional compartmentalized classifications. Accurate modeling requires taxonomies that reflect structural interdependence across economic, environmental, and institutional domains. This module introduces a multi-axial classification framework that maps how risk manifests, aggregates, and propagates through layered systems.

• Horizontal mapping captures cross-sectoral risks, such as climate volatility or biodiversity collapse, that radiate through entire economies irrespective of industry boundaries. These risks often originate from exogenous natural shocks or regulatory shifts and cannot be diversified away.
• Vertical mapping details sector-specific risks that can become systemic due to concentration, such as water dependency in agriculture, or cobalt reliance in energy storage. These risks begin as localized exposures but may amplify through upstream or downstream dependencies.
• Diagonal mapping identifies hybrid vulnerabilities, such as transition risk in fossil-intensive sovereigns, or labor unrest from social instability triggered by ecological collapse.

Taxonomies must also distinguish between endogenous systemic risks (e.g., financial fragility arising from carbon-intensive asset mispricing) and exogenous systemic risks (e.g., physical climate shocks).

To quantify these effects, nested typologies are used:

• Primary risks: direct stressors like flood exposure or carbon regulation
• Secondary transmission mechanisms: such as insurance market retraction, energy cost spikes, or migration
• Tertiary feedbacks: institutional erosion, credit downgrades, sovereign default, or regulatory backlash

Effective taxonomy supports modular integration into econometric, agent-based, and network simulation models for systemic forecasting.

Data Architecture and Structural Constraints The data environment surrounding sustainability risk is fragmented, often opaque, and structurally biased. ESG data does not emerge from a unified system but instead reflects competing frameworks, variable disclosure regimes, and provider methodologies.

• Architecture: ESG datasets are not built for systemic analysis. They are entity-centric, inconsistently scaled, and exhibit horizontal inconsistency across providers. Aggregation methodologies obscure directional signal. Disclosure data lacks verifiability, while scores embed value judgments that vary across rating agencies.
• Structural gaps:
• Temporal misalignment: Environmental metrics (e.g., emissions) lag by reporting cycles, while market data is real-time
• Spatial misalignment: Facility-level emissions versus country-level regulation
• Methodological opacity: ESG ratings often use proprietary weighting schemes that are not replicable or audit-friendly
• Survivorship bias: High-risk entities (e.g., small mining operations, sovereigns with poor governance) are systematically underrepresented

To mitigate these weaknesses, ESG datasets must be unbundled. Raw indicators should be extracted and standardized prior to use. Cross-referencing with public environmental data (e.g., GHG Protocol, national inventories, EPA datasets) and financial metrics (e.g., asset holdings, sovereign yield spreads, CDS premiums) is essential for calibration.

Environmental Indicators and Scenario-Based Datasets

Environmental datasets form the quantitative core of sustainability-related systemic risk models. These data are not merely descriptive but function as proxies for underlying ecological and resource constraints.

• Notable indicators:
• Emissions intensity by sector and geography
• Land-use change, deforestation rates, and agricultural expansion
• Energy mix and dependency ratios
• Water withdrawal per economic output unit
• Ecological footprint versus biocapacity at regional scale
• Soil fertility loss, fisheries depletion, and nitrogen imbalance

Sources include World Bank WDI, OECD Environment Statistics, FAO land and water data, EPI, and the Emissions Database for Global Atmospheric Research (EDGAR). However, the analytical frontier lies in the use of scenario-based datasets for systemic foresight modeling.

• NGFS scenarios provide regulatory-aligned macroeconomic pathways combining climate transition and physical risk, structured into Orderly, Disorderly, and Hot House World outcomes.
• IPCC SSP-RCP matrices offer socio-economic and emissions trajectories used in integrated assessment models. These allow for long-term projections of policy, population, technology, and emissions feedbacks.
• CDP and IEA data introduce voluntary and modeled disclosures at sector and company level. These include climate target coverage, technology adoption pathways, and decarbonization potential.
• IAM outputs are critical for translating physical climate parameters into financial stress vectors across industries and asset classes.

All scenario datasets must be evaluated for internal consistency, model assumptions, and regional disaggregation. Scenario integration is not a plug-and-play exercise (baseline assumptions must be aligned with model input parameters, including GDP growth, carbon pricing, and sector elasticity).

Lifecycle Assessment and Material Flow Analysis as Systemic Inputs

Standard ESG metrics rarely capture upstream or downstream ecological dependencies. Lifecycle assessment (LCA) and material flow analysis (MFA) are necessary to quantify embedded environmental risk across product and infrastructure lifespans.

• LCA, when properly applied, uncovers resource intensity, waste generation, and emissions across all phases of production, usage, and end-of-life. This is critical for risk modeling in heavy industries, infrastructure, and consumer goods, where indirect (scope 3) impacts dominate.
• Common indicators include cumulative energy demand, global warming potential, eutrophication, acidification, and land use.
• Methodologies should conform to ISO 14040/44 and utilize regionalized impact categories.
• MFA tracks material stocks and flows—such as lithium, copper, or rare earth elements—across economic systems. These flows often reveal systemic bottlenecks, import dependencies, or vulnerabilities to geopolitical disruption.

In systemic risk models, these assessments function as latent variable generators; they do not always show up in balance sheets, but they define long-term fragility in globalized production systems.

Cross-Domain Data Preprocessing and Harmonization

The integration of financial, climate, and environmental datasets introduces significant technical challenges in structure, format, and semantics. Without rigorous preprocessing, model outputs will be inconsistent or meaningless.

• Normalization: Align units (e.g., tons of CO₂ per dollar of revenue), convert nominal to real values, and rebase indices for cross-compatibility
• Interpolation and smoothing: Address missing time series points, especially for climate or emissions projections; utilize spline interpolation or Kalman filtering
• Temporal alignment: Synchronize reporting periods; match annual ESG data with high-frequency financial or economic data via lag structures or rolling averages
• Taxonomic reconciliation: Link company data to asset-level emissions and sectoral benchmarks; map product classifications to environmental input-output tables
• Dimensionality reduction: Employ PCA, t-SNE, or factor models to extract dominant systemic signals while preserving interpretability
• Bias detection: Conduct sensitivity analyses to detect distortion from reporting selection bias, survivorship bias, or geographic sampling gaps

Most systemic models fail not at the conceptual level, but at the data integration layer. Without cross-domain harmonization, simulation models will produce unstable, non-replicable, and policy-irrelevant outputs.